The structured arrangement of sounds in musical pieces, results in the unique creation of complex acoustic mixtures. The analysis of these mixtures, with the objective of estimating the individual sounds which constitute them, is known as musical instrument sound source separation, and has applications in audio coding, audio restoration, music production, music information retrieval and music education. This thesis principally addresses the issues related to the separation of harmonic musical instrument sound sources in single-channel mixtures. The contributions presented in this work include novel separation methods which exploit the characteristic structure and inherent correlations of pitched sound sources; as well as an exploration of the musical timbre space, for the development of an objective distortion metric to evaluate the perceptual quality of separated sources. The separation methods presented in this work address the concordant nature of musical mixtures using a model-based paradigm. Model parameters are estimated for each source, beginning with a novel, computationally efficient algorithm for the refinement of frequency estimates of the detected harmonics. Harmonic tracks are formed, and overlapping components are resolved by exploiting spectro-temporal intra-instrument dependencies, integrating the spectral and temporal approaches which are currently employed in a mutually exclusive manner in existing systems. Subsequent to the harmonic magnitude extraction using this method, a unique, closed-loop approach to source synthesis is presented, separating sources by iteratively minimizing the aggregate error of the sources, constraining the minimization to a set of estimated parameters. The proposed methods are evaluated independently, and then are placed within the context of a source separation system, which is evaluated using objective and subjective measures. The evaluation of music source separation systems is presently limited by the simplicity of objective measures, and the extensive effort required to conduct subjective evaluations. To contribute to the development of perceptually relevant evaluations, three psychoacoustic experiments are also presented, exploring the perceptual sensitivity of timbre for the development of an objective distortion metric for timbre. The experiments investigate spectral envelope sensitivity, spectral envelope morphing and noise sensitivity.

This study focuses on improving the quality of statistical speech synthesis based on sinusoidal models. Vocoders play a crucial role during the parametrisation and reconstruction process, so we first lead an experimental comparison of a broad range of the leading vocoder types. Although our study shows that for analysis / synthesis, sinusoidal models with complex amplitudes can generate high quality of speech compared with source-filter ones, component sinusoids are correlated with each other, and the number of parameters is also high and varies in each frame, which constrains its application for statistical speech synthesis. Therefore, we first propose a perceptually based dynamic sinusoidal model (PDM) to decrease and fix the number of components typically used in the standard sinusoidal model. Then, in order to apply the proposed vocoder with an HMM-based speech synthesis system (HTS), two strategies for modelling sinusoidal parameters have been compared. In the first method (DIR parameterisation), features extracted from the fixed- and low-dimensional PDM are statistically modelled directly. In the second method (INT parameterisation), we convert both static amplitude and dynamic slope from all the harmonics of a signal, which we term the Harmonic Dynamic Model (HDM), to intermediate parameters (regularised cepstral coefficients (RDC)) for modelling. Our results show that HDM with intermediate parameters can generate comparable quality to STRAIGHT. As correlations between features in the dynamic model cannot be modelled satisfactorily by a typical HMM-based system with diagonal covariance, we have applied and tested a deep neural network (DNN) for modelling features from these two methods. To fully exploit DNN capabilities, we investigate ways to combine INT and DIR at the level of both DNN modelling and waveform generation. For DNN training, we propose to use multi-task learning to model cepstra (from INT) and log amplitudes (from DIR) as primary and secondary tasks. We conclude from our results that sinusoidal models are indeed highly suited for statistical parametric synthesis. The proposed method outperforms the state-of-the-art STRAIGHT-based equivalent when used in conjunction with DNNs. To further improve the voice quality, phase features generated from the proposed vocoder also need to be parameterised and integrated into statistical modelling. Here, an alternative statistical model referred to as the complex-valued neural network (CVNN), which treats complex coefficients as a whole, is proposed to model complex amplitude explicitly. A complex-valued back-propagation algorithm using a logarithmic minimisation criterion which includes both amplitude and phase errors is used as a learning rule. Three parameterisation methods are studied for mapping text to acoustic features: RDC / real-valued log amplitude, complex-valued amplitude with minimum phase and complex-valued amplitude with mixed phase. Our results show the potential of using CVNNs for modelling both real and complex-valued acoustic features. Overall, this thesis has established competitive alternative vocoders for speech parametrisation and reconstruction. The utilisation of proposed vocoders on various acoustic models (HMM / DNN / CVNN) clearly demonstrates that it is compelling to apply them for the parametric statistical speech synthesis.

Chronic immune insult and immunosuppressant-related toxicities have remained an enduring challenge in organ transplantation. Long-term survival of transplant patients has improved marginally in recent decades due to these challenges. To circumvent these issues, transplant investigators have researched immune tolerance mechanisms that demonstrate potential to induce immunosuppression and rejection-free survival in the clinic. One mechanism in particular, the liver tolerance effect, has already demonstrated this experimentally and clinically. Liver transplants in experimental models and human patients have exhibited the ability to become spontaneously accepted without being rejected by the recipient’s immune system. Research in recent decades has revealed that the liver parenchymal and non-parenchymal cell populations harbor potent immunomodulatory properties. In the context of liver transplantation, it has been found that two cell populations in particular, the mesenchyme-derived liver sinusoidal endothelial cells and hepatic stellate cells, mediate the induction of liver transplant tolerance through a mechanism known as mesenchyme-mediated immune control.

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La modélisation sinusoïdale est une des méthodes les plus largement utilisés paramétriques pour la parole et le traitement des signaux audio. Inspiré par le récemment introduit Modèle aQHM et Modèle aHM, nous la vue d’ensemble de la théorie de l’ adaptation sinusoïdale modélisation et nous proposons un modèle nommé la Modèle eaQHM, qui est un non modèle paramétrique de mesure d’ajuster les amplitudes et les phases instantanées de ses fonctions de base aux caractéristiques variant dans le temps de sous-jacents du signal de parole, ainsi atténuer significativement la dite hypothèse de stationnarité locale. Le eaQHM est montré à surperformer aQHM dans l’analyse et la resynthèse de la parole voisée. Sur la base de la eaQHM , un système hybride d’analyse / synthèse de la parole est présenté (eaQHNM), et aussi d’ une version hybride de l’ aHM (aHNM). En outre, nous présentons la motivation pour une représentation pleine bande de la parole en utilisant le eaQHM, c’est, représentant toutes les parties du discours comme haute résolution des sinusoıdes AM-FM. Les expériences montrent que l’adaptation et la quasi-harmonicité est suffisante pour fournir une qualité de transparence dans la parole non voisée resynthèse. La pleine bande analyse eaQHM et système de synthèse est présenté à côté, ce qui surpasse l’état de l’art des systèmes, hybride ou pleine bande, dans la reconstruction de la parole, offrant une qualité transparente confirmé par des évaluations objectives et subjectives. En ce qui concerne les applications, le eaQHM et l’ aHM sont appliquées sur les modifications de la parole (de temps et pas mise à l’échelle). Les modifications qui en résultent sont de haute qualité, et suivent des règles très simples, par rapport à d’autres systèmes de modification état de l’art. Les résultats montrent que harmonicité est préféré au quasi- harmonicité de modifications de la parole du fait de la simplicité de la représentation intégrée. En outre, la pleine bande eaQHM est appliquée sur le problème de la modélisation des signaux audio, et en particulier d’instrument de musique retentit. Le eaQHM est évaluée et comparée à des systèmes à la pointe de la technologie, et leur est montré surpasser en termes de qualité de resynthèse, représentant avec succès l’attaque , transitoire, et une partie stationnaire d’un son d’instruments de musique. Enfin, une autre application est suggéré, à savoir l’analyse et la classification des discours émouvant. Le eaQHM est appliqué sur l’analyse des discours émouvant, offrant à ses paramètres instantanés comme des caractéristiques qui peuvent être utilisés dans la reconnaissance et la quantification vectorielle à base classification du contenu émotionnel de la parole. Bien que les modèles sinusoidaux sont pas couramment utilisés dans ces tâches, les résultats sont prometteurs. / Sinusoidal Modeling is one of the most widely used parametric methods for speech and audio signal processing. The accurate estimation of sinusoidal parameters (amplitudes, frequencies, and phases) is a critical task for close representation of the analyzed signal. In this thesis, based on recent advances in sinusoidal analysis, we propose high resolution adaptive sinusoidal models for analysis, synthesis, and modifications systems of speech. Our goal is to provide systems that represent speech in a highly accurate and compact way. Inspired by the recently introduced adaptive Quasi-Harmonic Model (aQHM) and adaptive Harmonic Model (aHM), we overview the theory of adaptive Sinusoidal Modeling and we propose a model named the extended adaptive Quasi-Harmonic Model (eaQHM), which is a non-parametric model able to adjust the instantaneous amplitudes and phases of its basis functions to the underlying time-varying characteristics of the speech signal, thus significantly alleviating the so-called local stationarity hypothesis. The eaQHM is shown to outperform aQHM in analysis and resynthesis of voiced speech. Based on the eaQHM, a hybrid analysis/synthesis system of speech is presented (eaQHNM), along with a hybrid version of the aHM (aHNM). Moreover, we present motivation for a full-band representation of speech using the eaQHM, that is, representing all parts of speech as high resolution AM-FM sinusoids. Experiments show that adaptation and quasi-harmonicity is sufficient to provide transparent quality in unvoiced speech resynthesis. The full-band eaQHM analysis and synthesis system is presented next, which outperforms state-of-the-art systems, hybrid or full-band, in speech reconstruction, providing transparent quality confirmed by objective and subjective evaluations. Regarding applications, the eaQHM and the aHM are applied on speech modifications (time and pitch scaling). The resulting modifications are of high quality, and follow very simple rules, compared to other state-of-the-art modification systems. Results show that harmonicity is preferred over quasi-harmonicity in speech modifications due to the embedded simplicity of representation. Moreover, the full-band eaQHM is applied on the problem of modeling audio signals, and specifically of musical instrument sounds. The eaQHM is evaluated and compared to state-of-the-art systems, and is shown to outperform them in terms of resynthesis quality, successfully representing the attack, transient, and stationary part of a musical instrument sound. Finally, another application is suggested, namely the analysis and classification of emotional speech. The eaQHM is applied on the analysis of emotional speech, providing its instantaneous parameters as features that can be used in recognition and Vector-Quantization-based classification of the emotional content of speech. Although the sinusoidal models are not commonly used in such tasks, results are promising.

Electrical drive systems are used in various applications and getting more attractive in recent years. When the usage of electric motors increased in different applications then the control model of them has been also demanded. This work is aimed at deeper research to gain a better understanding of three different control models for electric motors, in case of this work a brushless direct current (BLDC) motor. Three different types of control models (6-step, sinusoidal and FOC control) have been investigated and designed using MATLAB/SIMULINK. Then the control models have been implemented in an Arduino Due based BLDC motor and its functionality has been configured. The results show that the FOC control model provided to work better in the simulation while the implementation of the hardware showed that sinusoidal control works a little better and smoother. Making the implementation of the control models to the hardware work better requires more works and this has been left for future work.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23055号 / 医博第4682号 / 新制||医||1048(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 妹尾 浩, 教授 伊達 洋至, 教授 長船 健二 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

The core used to build power inductors and transformers are soft magnetic materials. When there is alternating external field, the magnetic moments rotate and consume energy, which is the core loss. The core loss depends on the AC flux frequency, amplitude, waveform, DC bias and temperature. These dependences are nonlinear and difficult to predict. How to measure, model and analyze the core loss is a challenge for decades. In this dissertation, two new core loss measurement methods are introduced first. These two methods use the reactive cancellation concept to reduce the sensitivity to phase discrepancy, which will destroy the accuracy in classic two-winding method for high frequency high quality factor sample measurements. By using the new measurement techniques the accuracy can be improved by several orders. The first is for sinusoidal waveforms, and the second is for non-sinusoidal wave. The new methods enable high frequency core loss characterization capability, which will help scientists and engineers on material research and inductor/transformer design. Measurement examples, considerations and error analysis are demonstrated and discussed in detail. With the measurement techniques, the core loss under rectangular AC voltage and DC bias current are investigated. A new core loss model named rectangular extension Steinmetz equation (RESE) is proposed based on the measurement results. The new model is shown to be more accurate than the existing core loss models. Several commercially available MnZn ferrites are characterized and modeled. Other than conventional MnZn ferrite materials, three commercial LTCC ferrite materials are characterized for integrated power supply applications. Based on characterized properties of these LTCCs, a group of new LTCC ferrites are fabricated and tested. The new LTCC is fabricated by laminating commercial LTCC tapes and co-firing. The new LTCC is demonstrated to have over 50% more inductance over the commercial LTCC materials. This work indicates that the power electronics engineers should work with material engineers to get the optimum material for a given application. In the last part, the core loss of the partially saturated lateral flux planar inductor is analyzed. The challenge of the analysis is the complexity of the distribution of bias field and flux density in a highly biased planar inductor. Each point in the core is working at different excitation and bias condition, and the core loss density is very non-uniform. The proposed method combines the characterization tested in previous chapters and the commercial finite element tool. Experiments verified that the calculation errors are within about 10%. In conclusion, the research in this dissertation proposed a complete solution to measure, model and analyze the high frequency core loss. This solution will not only facilitate fundamental research on physics understanding and material innovation, but also development of power electronics and RF applications. / Ph. D.

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